Object detection is often used in many computer vision based applications along with camera pose estimation to register the appearance of virtual objects so that real world images augmented with the virtual objects can be displayed. Camera pose refers to the position and orientation of the camera relative to a frame of reference. Registration, image registration or image alignment refers to the process of transforming and/or integrating different data sets to a single coordinate system or a common frame of reference.
Conventional tracking methods, which often use edge-based tracking, sample a large number of image pixels to detect potential edges. Moreover, the sampling is repeated for every image frame. Therefore, conventional edge-based trackers incur high computational overhead and suffer from increased tracking time because of the large number of edges tracked. The computational overhead and tracking time delays associated with conventional edge-based trackers limit their applicability.
Therefore, there is a need for efficient edge-based tracking methods that reduce the computational overhead and tracking time delays, while maintain reliability.